Introduction

This report analyzes metastatic melanoma data from MetMap project, as published in “A metastasis map of human cancer cell lines” by Jin, et al 2020., and is described in further detail on the MetMap website.

What are the gene programs the drive melanoma metastases?

The goals of this analysis are: 1) Identify which cell lines have high/low metastatic potential to specific organs. 1) Correlate metastatic potential (CI.95) with penetrance. 1) Compare across organs and cell lines. 1) Highlight patterns of multi-organ metastasis vs. organotropism.

Some Help Definitions

Here is a short explanation of some of the features that we’re about to dive into…

Metastatic potential

DNA barcode abundance detected in each organ relative to the pre-injected population, presented on a log10 scale, range from -4 to 4.

<= -4: non-metastatic
-4~-2: (weakly) metastatic, but with low confidence
>= -2: metastatic, with higher confidence

Penetrance

Percentage of animals that the cell lines were detected via barcode sequencing, ranges 0 to 1

Read and Process MetMap Data

The file Supplementary Table 03 MetMap cell line annotation.xlsx, which was downloaded from the Metmap download page contains the “metadata” describing all cell lines in this study. We will read this file here and select only the melanoma cell lines for further analysis.

read_excel("./data/metmap_data/Supplementary Table 03 MetMap cell line annotation.xlsx") %>%
  dplyr::filter(cancer_type == "melanoma") %>%
  as.data.frame() -> metmap_melanoma_metadata
# View(metmap_melanoma_metadata)

DT::datatable(metmap_melanoma_metadata)

We read the file Supplementary Table 04 MetMap 500 met potential.xlsx, which was downloaded from the Metmap download page and combine the different Excel sheets into a single dataframe:

# Normalize organ placement around a circle (petals at fixed angles)
# organ_positions <- data.frame(
#   organ = c("brain", "lung", "liver", "bone", "kidney"),
#   angle = seq(0, 2 * pi, length.out = 6)[1:5]  # 5 equally spaced directions
# )

sheet_names <- excel_sheets("./data/metmap_data/Supplementary Table 04 MetMap 500 met potential.xlsx")

dplyr::bind_rows(
  ## brain
  read_excel("./data/metmap_data/Supplementary Table 04 MetMap 500 met potential.xlsx",
             sheet = 1) %>%
    dplyr::rename(CCLE_name = names(.)[1]) %>%
    dplyr::mutate(organ = gsub("metp500\\.", "", sheet_names[1])),
  ## lung
  read_excel("./data/metmap_data/Supplementary Table 04 MetMap 500 met potential.xlsx",
             sheet = 2) %>%
    dplyr::rename(CCLE_name = names(.)[1]) %>%
    dplyr::mutate(organ = gsub("metp500\\.", "", sheet_names[2])),
  ## liver
  read_excel("./data/metmap_data/Supplementary Table 04 MetMap 500 met potential.xlsx",
             sheet = 3) %>%
    dplyr::rename(CCLE_name = names(.)[1]) %>%
    dplyr::mutate(organ = gsub("metp500\\.", "", sheet_names[3])),
  ## bone
  read_excel("./data/metmap_data/Supplementary Table 04 MetMap 500 met potential.xlsx",
             sheet = 4) %>%
    dplyr::rename(CCLE_name = names(.)[1]) %>%
    dplyr::mutate(organ = gsub("metp500\\.", "", sheet_names[4])),
  ## kidney
  read_excel("./data/metmap_data/Supplementary Table 04 MetMap 500 met potential.xlsx",
             sheet = 5) %>%
    dplyr::rename(CCLE_name = names(.)[1]) %>%
    dplyr::mutate(organ = gsub("metp500\\.", "", sheet_names[5]))) %>%
  dplyr::filter(CCLE_name %in% metmap_melanoma_metadata$CCLE_name) %>%
  # dplyr::left_join(organ_positions, by = "organ") %>%
  # dplyr::mutate(
  #   x0 = 0,  # center x
  #   y0 = 0,  # center y
  #   a = CI.95 / 2,         # semi-major axis = length of petal
  #   b = penetrance / 2,    # semi-minor axis = width of petal
  #   angle = angle          # angle of petal orientation
  # ) %>%
  as.data.frame() -> metmap_combined

## save the converted metadata table ( for Takis)
metmap_combined %>%
  dplyr::left_join(metmap_melanoma_metadata, by = "CCLE_name") %>%
  dplyr::select(contains("name"), contains("id"), everything()) %>%
  dplyr::select(-cancer_subtype) %>%
  as_tibble() %>%
  writexl::write_xlsx(path = "./data/metmap_melanoma_scores.xlsx")

# names(metmap_combined)
# unique(metmap_combined$organ)

DT::datatable(metmap_combined)

Visualizations

Distribution of Metastatic Potential (CI.95) Across Organs

metmap_combined %>%
  ggplot(aes(x = organ, y = CI.95, fill = organ)) +
    geom_violin() +
    geom_jitter(width = 0.2, alpha = 0.5) +
    geom_hline(yintercept = -2, color = "red", linetype = "dashed") +
    theme_minimal() +
    labs(
      title = "Distribution of Metastatic Potential (CI.95) Across Organs",
      x = "Organ",
      y = "CI.95") +
    theme(legend.position = "none")

metmap_combined %>%
  ggplot(aes(x = organ, y = CI.95, fill = organ)) +
    geom_boxplot() +
    geom_jitter(width = 0.2, alpha = 0.5) +
    geom_hline(yintercept = -2, color = "red", linetype = "dashed") +
    theme_minimal() +
    labs(
      title = "Distribution of Metastatic Potential (CI.95) Across Organs",
      x = "Organ",
      y = "CI.95") +
    theme(legend.position = "none")

Distribution of Penetrance Across Organs

metmap_combined %>%
  ggplot(aes(x = organ, y = penetrance, fill = organ)) +
    geom_violin() +
    geom_jitter(width = 0.2, alpha = 0.5) +
    geom_hline(yintercept = -2, color = "red", linetype = "dashed") +
    theme_minimal() +
    labs(
      title = "Distribution of Penetrance Across Organs",
      x = "Organ",
      y = "Penetrance") +
    theme(legend.position = "none")

metmap_combined %>%
  ggplot(aes(x = organ, y = penetrance, fill = organ)) +
    geom_boxplot() +
    geom_jitter(width = 0.2, alpha = 0.5) +
    geom_hline(yintercept = -2, color = "red", linetype = "dashed") +
    theme_minimal() +
    labs(
      title = "Distribution of Penetrance Across Organs",
      x = "Organ",
      y = "Penetrance") +
    theme(legend.position = "none")

Waterfall Plot - Metastatic Potential (CI.95)

metmap_combined %>% 
  dplyr::arrange(desc(CI.95)) %>%
  dplyr::mutate(rank = row_number()) %>% 
  ggplot(aes(x = rank, y = CI.95, fill = organ)) +
    geom_col() +
    geom_hline(yintercept = -2, color = "red", linetype = "dashed") +
    theme_classic() +
    labs(title = "Waterfall Plot of Metastatic Potential (CI.95)", 
         x = "Ranked Cell Line Samples", 
         y = "CI.95 (Metastatic Potential)", 
         fill = "Organ") +
    theme(axis.text.x = element_blank(),
          axis.ticks.x = element_blank()) -> p1

# plotly::ggplotly(p1)
p1

Waterfall Plot - Penetrance

metmap_combined %>% 
  dplyr::arrange(penetrance) %>%
  dplyr::mutate(rank = row_number()) %>% 
  ggplot(aes(x = rank, y = penetrance, fill = organ)) +
    geom_col() +
    geom_hline(yintercept = -2, color = "red", linetype = "dashed") +
    theme_classic() +
    labs(title = "Waterfall Plot of Penetrance",
         x = "Ranked Cell Line Samples",
         y = "Penetrance",
         fill = "Organ") +
    theme(axis.text.x = element_blank(),
          axis.ticks.x = element_blank()) -> p2

# plotly::ggplotly(p2)
p2

Heatmap - Metastatic Potential (CI.95)

metmap_combined %>% 
  dplyr::select(CCLE_name, CI.95, organ) %>% 
  tidyr::pivot_wider(
    names_from = organ,
    values_from = CI.95) %>%
  tibble::column_to_rownames("CCLE_name") %>% 
  as.data.frame() -> df_meta

metmap_scaled1 <- t(scale(t(df_meta)))  # scale rows, not columns
metmap_scaled1[is.nan(metmap_scaled1)] <- 0

pheatmap(metmap_scaled1,
         fontsize_row = 6,
         cluster_rows = TRUE, 
         cluster_cols = TRUE,
         color = colorRampPalette(c("blue", "white", "red"))(100),
         main = "Z-scored CI.95 Metastatic Potential")

What are “high metastatic potential” cell lines globally, and by organ?

unique(metmap_combined$CCLE_name)
#>  [1] "HS944T_SKIN"    "MELHO_SKIN"     "HT144_SKIN"     "SH4_SKIN"      
#>  [5] "G361_SKIN"      "COLO679_SKIN"   "A375_SKIN"      "IGR37_SKIN"    
#>  [9] "A2058_SKIN"     "IGR1_SKIN"      "A101D_SKIN"     "HS939T_SKIN"   
#> [13] "RVH421_SKIN"    "HS852T_SKIN"    "SKMEL30_SKIN"   "COLO792_SKIN"  
#> [17] "LOXIMVI_SKIN"   "CHL1_SKIN"      "CJM_SKIN"       "WM983B_SKIN"   
#> [21] "COLO783_SKIN"   "MEWO_SKIN"      "K029AX_SKIN"    "MELJUSO_SKIN"  
#> [25] "COLO800_SKIN"   "IPC298_SKIN"    "WM88_SKIN"      "COLO741_SKIN"  
#> [29] "UACC257_SKIN"   "SKMEL5_SKIN"    "SKMEL24_SKIN"   "WM793_SKIN"    
#> [33] "UACC62_SKIN"    "MALME3M_SKIN"   "MDAMB435S_SKIN" "HS294T_SKIN"   
#> [37] "WM1799_SKIN"    "WM2664_SKIN"    "SKMEL2_SKIN"    "SKMEL3_SKIN"

Heatmap - Penetrance

metmap_combined %>% 
  dplyr::select(CCLE_name, penetrance, organ) %>% 
  tidyr::pivot_wider(
    names_from = organ,
    values_from = penetrance) %>%
  tibble::column_to_rownames("CCLE_name") %>% 
  as.data.frame() -> df_pen

metmap_scaled2 <- t(scale(t(df_pen)))  # scale rows, not columns
metmap_scaled2[is.nan(metmap_scaled2)] <- 0

pheatmap(metmap_scaled2,
         fontsize_row = 6,
         cluster_rows = TRUE, 
         cluster_cols = TRUE,
         color = colorRampPalette(c("blue", "white", "red"))(100),
         main = "Z-scored Penetrance")

What are “high penetrance” cell lines globally, and by organ?

unique(metmap_combined$CCLE_name)
#>  [1] "HS944T_SKIN"    "MELHO_SKIN"     "HT144_SKIN"     "SH4_SKIN"      
#>  [5] "G361_SKIN"      "COLO679_SKIN"   "A375_SKIN"      "IGR37_SKIN"    
#>  [9] "A2058_SKIN"     "IGR1_SKIN"      "A101D_SKIN"     "HS939T_SKIN"   
#> [13] "RVH421_SKIN"    "HS852T_SKIN"    "SKMEL30_SKIN"   "COLO792_SKIN"  
#> [17] "LOXIMVI_SKIN"   "CHL1_SKIN"      "CJM_SKIN"       "WM983B_SKIN"   
#> [21] "COLO783_SKIN"   "MEWO_SKIN"      "K029AX_SKIN"    "MELJUSO_SKIN"  
#> [25] "COLO800_SKIN"   "IPC298_SKIN"    "WM88_SKIN"      "COLO741_SKIN"  
#> [29] "UACC257_SKIN"   "SKMEL5_SKIN"    "SKMEL24_SKIN"   "WM793_SKIN"    
#> [33] "UACC62_SKIN"    "MALME3M_SKIN"   "MDAMB435S_SKIN" "HS294T_SKIN"   
#> [37] "WM1799_SKIN"    "WM2664_SKIN"    "SKMEL2_SKIN"    "SKMEL3_SKIN"

Correlation Between Metastatic Potential and Penetrance

metmap_combined %>%
  ggplot(aes(x = CI.95, y = penetrance, color = organ)) +
    geom_point(alpha = 0.7) +
    geom_vline(xintercept = -2, color = "red", linetype = "dashed") +
    geom_smooth(method = "lm", se = FALSE, linetype = "dashed") +
    # facet_wrap(~ organ) +
    theme_minimal() +
    stat_cor(method = "spearman") +
    labs(
      title = "Correlation between Metastatic Potential (CI.95) and Penetrance",
      x = "Metastatic Potential (CI.95)",
      y = "Penetrance")

Global Correlation Between Metastatic Potential and Penetrance

metmap_combined %>%
  ggplot(aes(x = CI.95, y = penetrance)) +
    geom_point(alpha = 0.7) +
    geom_vline(xintercept = -2, color = "red", linetype = "dashed") +
    geom_smooth(method = "lm", se = FALSE, linetype = "dashed") +
    # facet_wrap(~ organ) +
    theme_minimal() +
    stat_cor(method = "spearman") +
    labs(
      title = "Correlation between Metastatic Potential (CI.95) and Penetrance",
      x = "Metastatic Potential (CI.95)",
      y = "Penetrance") #-> p4

# plotly::ggplotly(p4)

Faceted Correlation Between Metastatic Potential and Penetrance

Below we create faceted scatter plots with regression line for each organ. The vertical red dashed line at Metastatic Potential = -2 represents where cell lines are considered “metastatic” or not.

metmap_combined %>%
  ggplot(aes(x = CI.95, y = penetrance, color = organ)) +
    geom_point(alpha = 0.7) +
    geom_vline(xintercept = -2, color = "red", linetype = "dashed") +
    geom_smooth(method = "lm", se = FALSE, linetype = "dashed") +
    facet_wrap(~ organ) +
    theme_minimal() +
    stat_cor(method = "spearman", label.x = -4, label.y = 1.05, size = 3.5) +
    labs(
      title = "Correlation between Metastatic Potential (CI.95) and Penetrance",
      x = "Metastatic Potential (CI.95)",
      y = "Penetrance")

Rose Plots

# Ensure organ levels are ordered
organ_levels <- c("brain", "lung", "liver", "bone", "kidney")

# Normalize angles for organs around the circle using penetrance as angular width
 metmap_combined %>%
  dplyr::mutate(organ = factor(organ, levels = organ_levels)) %>%
  dplyr::group_by(CCLE_name) %>%
  # Calculate cumulative angles per cell line
  dplyr::mutate(
    total_penetrance = sum(penetrance),
    fraction = penetrance / total_penetrance,
    angle_width = 2 * pi * fraction,
    angle_start = cumsum(lag(angle_width, default = 0)),
    angle_end = angle_start + angle_width,
    r0 = 0,
    r = CI.95) %>% ungroup() -> metmap_arc

# Plot using geom_arc_bar
ggplot(metmap_arc) +
  geom_arc_bar(
    aes(
      x0 = 0, y0 = 0,
      r0 = r0, r = r,
      start = angle_start, end = angle_end,
      fill = organ),
    color = "black", alpha = 0.8) +
  coord_fixed() +
  facet_wrap(~CCLE_name, ncol = 5) +
  theme_minimal() +
  theme(
    strip.text = element_text(size = 8),
    axis.title = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    panel.grid = element_blank()) +
  labs(
    title = "Rose plots of MetMap melanoma cell lines by metastatized organ",
    fill = "Organ")

Clustering or Organ Tropism Classification

Test/Approach: Hierarchical clustering or UMAP

Purpose: Reveal whether certain cell lines cluster by metastatic behavior across organs.

umap_result <- umap(metmap_scaled1)
umap_df <- as.data.frame(umap_result$layout)
umap_df$CCLE_name <- rownames(df_meta)
ggplot(umap_df, aes(x = V1, y = V2, label = CCLE_name)) +
  geom_point(alpha = 0.7) +
    geom_text_repel(size = 3, max.overlaps = 50) +
  theme_minimal() +
  labs(title = "UMAP of Cell Lines by Metastatic Potential",
       x = "UMAP1", y = "UMAP2")

umap_result <- umap(metmap_scaled2)
umap_df <- as.data.frame(umap_result$layout)
umap_df$CCLE_name <- rownames(df_meta)
ggplot(umap_df, aes(x = V1, y = V2, label = CCLE_name)) +
  geom_point(alpha = 0.7) +
  geom_text_repel(size = 3, max.overlaps = 50) +
  theme_minimal() +
  labs(title = "UMAP of Cell Lines by Penetrance",
       x = "UMAP1", y = "UMAP2")

rownames(metmap_scaled1) <- paste0("CI.95_", rownames(metmap_scaled1))
rownames(metmap_scaled2)   <- paste0("pen_", rownames(metmap_scaled2))
joint_matrix <- cbind(metmap_scaled1, metmap_scaled2)
joint_scaled <- scale(joint_matrix)
umap_result <- umap(joint_scaled)
umap_df <- as.data.frame(umap_result$layout)
umap_df$CCLE_name <- rownames(df_meta)
ggplot(umap_df, aes(x = V1, y = V2, label = CCLE_name)) +
  geom_point(alpha = 0.7) +
  geom_text_repel(size = 3, max.overlaps = 50) +
  theme_minimal() +
  labs(title = "Joint UMAP of Cell Lines by Penetrance and Metastatic Potential",
       x = "UMAP1", y = "UMAP2")

PCA

# joint_scaled

# Perform PCA using prcomp()
pca_result <- prcomp(joint_scaled, center = TRUE, scale. = TRUE)

# Summary of variance explained
# summary(pca_result)

# Access principal components
pca_scores <- as.data.frame(pca_result$x)
pca_scores$CCLE_name <- rownames(df_meta)

ggplot(pca_scores, aes(x = PC1, y = PC2, label = CCLE_name)) +
  geom_point(alpha = 0.7) +
  geom_text_repel(size = 3, max.overlaps = 50) +
  theme_minimal() +
  labs(
    title = "PCA of Numeric Data",
    x = "Principal Component 1",
    y = "Principal Component 2"
  )

Statistical Tests

We will run various statistical tests to determine the nature of the relationships between metastatic potential, penetrance and organ in melanoma cell lines. The following is a little background on the statistical tests that we will perform:

Kruskal-Wallis Test (Global Non-Parametric Test)

Purpose: To test whether there is a statistically significant difference in the distribution of a continuous variable (like CI.95 or penetrance) across more than two independent groups (e.g., organs: brain, liver, lung, etc.).

Why use Kruskal-Wallis instead of ANOVA?
```
 The variable (e.g., CI.95) is log-transformed and likely non-normally distributed.

 Kruskal-Wallis is a non-parametric test and does not assume normality or equal variance.
```
What it tells you: At least one group differs from the others — but not which one(s).
Dunn’s Test (Post-hoc Pairwise Comparisons)

Purpose: To identify which specific pairs of groups differ after a Kruskal-Wallis test has found a significant global difference.

Why not use multiple Wilcoxon tests directly?

Running many pairwise tests inflates the Type I error rate (false positives).
Bonferroni Correction (Control for Multiple Testing)

Why needed: We’re doing multiple pairwise comparisons (e.g., brain vs. liver, brain vs. lung, etc.).

What it does: Adjusts the p-value threshold to keep the family-wise error rate controlled.

For example, if you do 10 tests, Bonferroni adjusts the significance threshold from 0.05 to 0.005.

Effect: More conservative (reduces false positives, may increase false negatives).

Global Spearman Correlation of Metastatic Potential and Penetrance

# Global correlation
cor_test_global <- cor.test(metmap_combined$CI.95, metmap_combined$penetrance, method = "spearman")
print(cor_test_global)
#> 
#>  Spearman's rank correlation rho
#> 
#> data:  metmap_combined$CI.95 and metmap_combined$penetrance
#> S = 184049, p-value < 2.2e-16
#> alternative hypothesis: true rho is not equal to 0
#> sample estimates:
#>     rho 
#> 0.86196

Per-Organ Spearman Correlation of Metastatic Potential and Penetrance

cor_results_per_organ <- metmap_combined %>%
  dplyr::group_by(organ) %>%
  dplyr::summarize(
    spearman_rho = cor(CI.95, penetrance, method = "spearman"),
    p_value = cor.test(CI.95, penetrance, method = "spearman")$p.value)

print(cor_results_per_organ)
#> # A tibble: 5 × 3
#>   organ  spearman_rho  p_value
#>   <chr>         <dbl>    <dbl>
#> 1 bone          0.920 4.65e-17
#> 2 brain         0.867 4.54e-13
#> 3 kidney        0.899 3.66e-15
#> 4 liver         0.915 1.36e-16
#> 5 lung          0.825 6.06e-11

Comparing Metastatic Potential Between Organs

Test: Kruskal-Wallis test (non-parametric)

Why: CI.95 is continuous, log-transformed, and likely non-normal. We're comparing distributions across multiple organs.

Post-hoc: Dunn's test with Bonferroni correction.

# Kruskal-Wallis test for CI.95 across organs
kruskal_result <- kruskal.test(CI.95 ~ organ, data = metmap_combined)
print(kruskal_result)
#> 
#>  Kruskal-Wallis rank sum test
#> 
#> data:  CI.95 by organ
#> Kruskal-Wallis chi-squared = 2.4835, df = 4, p-value = 0.6476

# Dunn's test with Bonferroni correction
dunn_result <- dunnTest(CI.95 ~ organ, data = metmap_combined, method = "bonferroni")
print(dunn_result)
#>        Comparison           Z   P.unadj P.adj
#> 1    bone - brain -0.43271014 0.6652254     1
#> 2   bone - kidney -1.24017817 0.2149095     1
#> 3  brain - kidney -0.80746803 0.4193969     1
#> 4    bone - liver -0.71957378 0.4717875     1
#> 5   brain - liver -0.28686364 0.7742167     1
#> 6  kidney - liver  0.52060439 0.6026424     1
#> 7     bone - lung -1.33097003 0.1831989     1
#> 8    brain - lung -0.89825989 0.3690470     1
#> 9   kidney - lung -0.09079186 0.9276580     1
#> 10   liver - lung -0.61139625 0.5409373     1

Penetrance Differences Between Organs

Test: Kruskal-Wallis or ANOVA (depending on distribution)

Alt: Chi-squared or Fisher's exact test if binarized penetrance (e.g. high ≥ 0.5, low < 0.5).

# Kruskal-Wallis test across organs
kruskal_penetrance <- kruskal.test(penetrance ~ organ, data = metmap_combined)
print(kruskal_penetrance)
#> 
#>  Kruskal-Wallis rank sum test
#> 
#> data:  penetrance by organ
#> Kruskal-Wallis chi-squared = 13.725, df = 4, p-value = 0.008228

# Dunn's test with Bonferroni correction
dunn_result <- dunnTest(penetrance ~ organ, data = metmap_combined, method = "bonferroni")
print(dunn_result)
#>        Comparison          Z     P.unadj      P.adj
#> 1    bone - brain  0.2600872 0.794796486 1.00000000
#> 2   bone - kidney -0.5338120 0.593471611 1.00000000
#> 3  brain - kidney -0.7938992 0.427254097 1.00000000
#> 4    bone - liver -1.6423486 0.100517781 1.00000000
#> 5   brain - liver -1.9024358 0.057114201 0.57114201
#> 6  kidney - liver -1.1085366 0.267630143 1.00000000
#> 7     bone - lung -2.9106391 0.003606903 0.03606903
#> 8    brain - lung -3.1707264 0.001520583 0.01520583
#> 9   kidney - lung -2.3768272 0.017462269 0.17462269
#> 10   liver - lung -1.2682906 0.204694209 1.00000000

Session Info

sessionInfo()
#> R version 4.5.0 (2025-04-11)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.0 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Europe/Brussels
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] grid      stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] DT_0.33            umap_0.2.10.0      ggpubr_0.6.0       plotly_4.10.4     
#>  [5] pheatmap_1.0.13    FSA_0.10.0         scales_1.4.0       ggforce_0.4.2     
#>  [9] ggvenn_0.1.10      readr_2.1.5        writexl_1.5.4      readxl_1.4.5      
#> [13] ggrepel_0.9.6.9999 ggplot2_3.5.2      tibble_3.2.1       tidyr_1.3.1       
#> [17] dplyr_1.1.4       
#> 
#> loaded via a namespace (and not attached):
#>  [1] gtable_0.3.6       xfun_0.52          bslib_0.9.0        htmlwidgets_1.6.4 
#>  [5] rstatix_0.7.2      lattice_0.22-7     tzdb_0.5.0         crosstalk_1.2.1   
#>  [9] vctrs_0.6.5        tools_4.5.0        generics_0.1.4     pkgconfig_2.0.3   
#> [13] Matrix_1.7-3       data.table_1.17.4  RColorBrewer_1.1-3 lifecycle_1.0.4   
#> [17] compiler_4.5.0     farver_2.1.2       carData_3.0-5      htmltools_0.5.8.1 
#> [21] sass_0.4.10        yaml_2.3.10        lazyeval_0.2.2     Formula_1.2-5     
#> [25] pillar_1.10.2      car_3.1-3          jquerylib_0.1.4    MASS_7.3-65       
#> [29] openssl_2.3.3      cachem_1.1.0       dunn.test_1.3.6    abind_1.4-8       
#> [33] nlme_3.1-168       RSpectra_0.16-2    tidyselect_1.2.1   digest_0.6.37     
#> [37] purrr_1.0.4        splines_4.5.0      labeling_0.4.3     polyclip_1.10-7   
#> [41] fastmap_1.2.0      cli_3.6.5          magrittr_2.0.3     utf8_1.2.5        
#> [45] dichromat_2.0-0.1  broom_1.0.8        withr_3.0.2        backports_1.5.0   
#> [49] rmarkdown_2.29     httr_1.4.7         reticulate_1.42.0  ggsignif_0.6.4    
#> [53] cellranger_1.1.0   png_0.1-8          askpass_1.2.1      hms_1.1.3         
#> [57] evaluate_1.0.3     knitr_1.50         viridisLite_0.4.2  mgcv_1.9-3        
#> [61] rlang_1.1.6        Rcpp_1.0.14        glue_1.8.0         tweenr_2.0.3      
#> [65] rstudioapi_0.17.1  jsonlite_2.0.0     R6_2.6.1

Meta-analysis of MetMap Datasets

Theo Killian

2025-06-08